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arXiv NLP research summaries for January 16, 2024.
Today's Research Themes (AI-Generated):
• Automating prompt engineering for LLMs enhances efficiency with PRewrite based on Reinforcement Learning.
• MARIO pipeline advances mathematical reasoning in LLMs with a novel dataset and fine-tuning protocol.
• New attack model exposes the vulnerability of multilingual text classifiers to generative adversarial attacks.
• Null-shot prompting leverages LLM hallucinations to improve performance on tasks like reading comprehension.
• DAPT framework showcases a dual attention approach for continual learning in LLMs, mitigating catastrophic forgetting.
By Brad EdwardsarXiv NLP research summaries for January 16, 2024.
Today's Research Themes (AI-Generated):
• Automating prompt engineering for LLMs enhances efficiency with PRewrite based on Reinforcement Learning.
• MARIO pipeline advances mathematical reasoning in LLMs with a novel dataset and fine-tuning protocol.
• New attack model exposes the vulnerability of multilingual text classifiers to generative adversarial attacks.
• Null-shot prompting leverages LLM hallucinations to improve performance on tasks like reading comprehension.
• DAPT framework showcases a dual attention approach for continual learning in LLMs, mitigating catastrophic forgetting.